Davids048andClaude Fable 5.1
a5aa64ab6b
VideoGenerator.from_pretrained(model_path, config) takes the same nested
settings as from_config; the 24 flat keywords, from_pretrained_kwargs_to_config,
FROM_PRETRAINED_KWARGS, and every flat_field in the schema are deleted.
nvfp4_fa4 is the typed field engine.attention.nvfp4_fa4, applied by a
resolution step. 65 call sites and the docs use the nested form; the three
kwargs golden cases are config cases with byte-identical results.
Every value is decided during resolution; no runtime code calls
with_override. The device offload policy (unified memory, MPS, layerwise
conflicts, lazy module load) runs as resolution steps in the main process
(fastvideo/api/device_policy.py), and the worker runs on the config it
receives. The LTX-2 refine defaults from model_index.json and the MiniMax-H3
schedule from fastvideo_inference.json are resolution steps
(fastvideo/api/checkpoint_defaults.py) that read local checkpoints or download
only those files; the H3 pipeline validates the schedule against the loaded
schedulers but no longer writes it. The preprocessing entry point passes the
downloaded local path as run state instead of a config override. Teacher and
critic models load with override_transformer_cls_name as a load argument.
The test isolation turns the device policy off, as it blocks downloads, so
that the goldens do not depend on the machine.
Against the 3f6893a0 goldens, every field still matches except boundary_ratio
and ltx2_vae_tiling (DESIGN section 9) and the fields that no longer exist.
The launcher and trainer YAML verifications report 0 unexplained differences.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
126 lines
4.3 KiB
Python
126 lines
4.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""
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Basic inference script for HunyuanGameCraft video generation.
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HunyuanGameCraft generates game-like videos with camera/action control.
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It takes an optional image input and generates video with camera motion
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based on simple action commands (forward, left, right, backward, rotations).
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Available actions:
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- forward (w): Move camera forward
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- backward (s): Move camera backward
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- left (a): Move camera left (strafe)
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- right (d): Move camera right (strafe)
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- left_rot: Rotate camera left (pan)
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- right_rot: Rotate camera right (pan)
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- up_rot: Rotate camera up (tilt)
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- down_rot: Rotate camera down (tilt)
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T2V vs I2V:
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- Default: I2V (uses a default reference image). Set GAMECRAFT_I2V_IMAGE to a
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URL or path to use a different image.
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- T2V only (no reference image): run with GAMECRAFT_I2V_IMAGE= (empty).
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"""
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import os
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import torch
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from fastvideo import VideoGenerator
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from fastvideo.models.camera import create_camera_trajectory
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# Model configuration (use GAMECRAFT_MODEL_PATH for local weights)
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MODEL_PATH = os.environ.get("GAMECRAFT_MODEL_PATH", "FastVideo/HunyuanGameCraft-Diffusers")
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# Default prompts for demo
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DEFAULT_PROMPTS = {
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"village":
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"A charming medieval village with cobblestone streets, thatched-roof houses, and vibrant flower gardens under a bright blue sky.",
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"temple":
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"A majestic ancient temple stands under a clear blue sky, its grandeur highlighted by towering Doric columns and intricate architectural details.",
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"forest":
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"A lush green forest with tall trees, dappled sunlight filtering through the leaves, and a winding dirt path.",
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"beach": "A tropical beach with crystal clear turquoise water, white sand, and palm trees swaying in the breeze.",
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}
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# I2V: default reference image (URL). Can override with a local path.
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DEFAULT_I2V_IMAGE_URL = ("https://huggingface.co/datasets/huggingface/documentation-images/"
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"resolve/main/diffusers/astronaut.jpg")
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DEFAULT_I2V_PROMPT = ("An astronaut hatching from an egg, on the surface of the moon, "
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"the darkness and depth of space realised in the background.")
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OUTPUT_PATH = "video_samples_gamecraft"
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def main():
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# Initialize generator
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# FastVideo will automatically download weights from HuggingFace
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generator = VideoGenerator.from_pretrained(
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MODEL_PATH,
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{
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"engine": {
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"num_gpus": 1,
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"use_fsdp_inference": True,
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"offload": {
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"dit": True,
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"vae": True,
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"text_encoder": True,
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"pin_cpu_memory": True,
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},
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},
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},
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)
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# Video parameters
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height = 704
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width = 1280
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num_frames = 33
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action = "forward"
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action_speed = 0.2
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# Create camera trajectory (Plücker coordinates)
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camera_states = create_camera_trajectory(
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action=action,
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height=height,
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width=width,
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num_frames=num_frames,
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action_speed=action_speed,
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dtype=torch.bfloat16,
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)
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print(f"Camera states shape: {camera_states.shape}")
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# I2V vs T2V: unset GAMECRAFT_I2V_IMAGE -> I2V (default image). Set to "" -> T2V.
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env_image = os.environ.get("GAMECRAFT_I2V_IMAGE")
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if env_image is None:
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image_path = DEFAULT_I2V_IMAGE_URL # default: I2V
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elif env_image.strip() == "":
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image_path = None # T2V
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else:
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image_path = env_image.strip() # I2V with given URL/path
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is_i2v = image_path is not None
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prompt = DEFAULT_I2V_PROMPT if is_i2v else DEFAULT_PROMPTS["temple"]
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print(f"Mode: {'I2V' if is_i2v else 'T2V'}, prompt: {prompt[:60]}...")
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request = {
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"prompt": prompt,
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"negative_prompt": "",
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"sampling": {
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"height": height,
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"width": width,
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"num_frames": num_frames,
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"num_inference_steps": 50,
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"guidance_scale": 6.0,
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"seed": 42,
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"fps": 24,
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},
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"output": {"output_path": OUTPUT_PATH, "save_video": True},
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"extensions": {"camera_states": camera_states},
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}
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if is_i2v:
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request["inputs"] = {"image_path": image_path}
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generator.generate(request)
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if __name__ == "__main__":
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main()
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